Python get_cat_dog_trainset 예제들

예제 #1

파일: main.py 프로젝트: DianaShatunova/NEUCOGAR

def trained_cat_dog_RFCNN():
    n = RecurrentNetwork()

    d = get_cat_dog_trainset()
    input_size = d.getDimension('input')
    n.addInputModule(LinearLayer(input_size, name='in'))
    n.addModule(SigmoidLayer(input_size+1500, name='hidden'))
    n.addOutputModule(LinearLayer(2, name='out'))
    n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
    n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
    n.addRecurrentConnection(FullConnection(n['out'], n['hidden'], name='nmc'))
    n.sortModules()

    t = BackpropTrainer(n, d, learningrate=0.0001)#, momentum=0.75)

    count = 0
    while True:
        globErr = t.train()
        print globErr
        count += 1
        if globErr < 0.01:
            break
        if count == 30:
            break

    exportCatDogRFCNN(n)
    return n

예제 #2

파일: main.py 프로젝트: magnastrazh/NEUCOGAR

def trained_cat_dog_RFCNN():
    n = RecurrentNetwork()

    d = get_cat_dog_trainset()
    input_size = d.getDimension('input')
    n.addInputModule(LinearLayer(input_size, name='in'))
    n.addModule(SigmoidLayer(input_size + 1500, name='hidden'))
    n.addOutputModule(LinearLayer(2, name='out'))
    n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
    n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
    n.addRecurrentConnection(FullConnection(n['out'], n['hidden'], name='nmc'))
    n.sortModules()

    t = BackpropTrainer(n, d, learningrate=0.0001)  #, momentum=0.75)

    count = 0
    while True:
        globErr = t.train()
        print globErr
        count += 1
        if globErr < 0.01:
            break
        if count == 30:
            break

    exportCatDogRFCNN(n)
    return n

예제 #3

파일: main.py 프로젝트: DianaShatunova/NEUCOGAR

def trained_cat_dog_ANN():
    n = FeedForwardNetwork()
    d = get_cat_dog_trainset()
    input_size = d.getDimension('input')
    n.addInputModule(LinearLayer(input_size, name='in'))
    n.addModule(SigmoidLayer(input_size+1500, name='hidden'))
    n.addOutputModule(LinearLayer(2, name='out'))
    n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
    n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
    n.sortModules()
    n.convertToFastNetwork()
    print 'successful converted to fast network'
    t = BackpropTrainer(n, d, learningrate=0.0001)#, momentum=0.75)

    count = 0
    while True:
        globErr = t.train()
        print globErr
        count += 1
        if globErr < 0.01:
            break
        if count == 30:
            break


    exportCatDogANN(n)
    return n

예제 #4

파일: main.py 프로젝트: magnastrazh/NEUCOGAR

def trained_cat_dog_ANN():
    n = FeedForwardNetwork()
    d = get_cat_dog_trainset()
    input_size = d.getDimension('input')
    n.addInputModule(LinearLayer(input_size, name='in'))
    n.addModule(SigmoidLayer(input_size + 1500, name='hidden'))
    n.addOutputModule(LinearLayer(2, name='out'))
    n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
    n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
    n.sortModules()
    n.convertToFastNetwork()
    print 'successful converted to fast network'
    t = BackpropTrainer(n, d, learningrate=0.0001)  #, momentum=0.75)

    count = 0
    while True:
        globErr = t.train()
        print globErr
        count += 1
        if globErr < 0.01:
            break
        if count == 30:
            break

    exportCatDogANN(n)
    return n